# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.

Usage - sources:
    $ yolo mode=predict model=yolo11n.pt source=0                               # webcam
                                                img.jpg                         # image
                                                vid.mp4                         # video
                                                screen                          # screenshot
                                                path/                           # directory
                                                list.txt                        # list of images
                                                list.streams                    # list of streams
                                                'path/*.jpg'                    # glob
                                                'https://youtu.be/LNwODJXcvt4'  # YouTube
                                                'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP, TCP stream

Usage - formats:
    $ yolo mode=predict model=yolo11n.pt                 # PyTorch
                              yolo11n.torchscript        # TorchScript
                              yolo11n.onnx               # ONNX Runtime or OpenCV DNN with dnn=True
                              yolo11n_openvino_model     # OpenVINO
                              yolo11n.engine             # TensorRT
                              yolo11n.mlpackage          # CoreML (macOS-only)
                              yolo11n_saved_model        # TensorFlow SavedModel
                              yolo11n.pb                 # TensorFlow GraphDef
                              yolo11n.tflite             # TensorFlow Lite
                              yolo11n_edgetpu.tflite     # TensorFlow Edge TPU
                              yolo11n_paddle_model       # PaddlePaddle
                              yolo11n.mnn                # MNN
                              yolo11n_ncnn_model         # NCNN
                              yolo11n_imx_model          # Sony IMX
                              yolo11n_rknn_model         # Rockchip RKNN
"""

import platform
import re
import threading
from pathlib import Path
from typing import Any, Dict, List, Optional, Union

import cv2
import numpy as np
import torch

from ultralytics.cfg import get_cfg, get_save_dir
from ultralytics.data import load_inference_source
from ultralytics.data.augment import LetterBox
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.utils import DEFAULT_CFG, LOGGER, MACOS, WINDOWS, callbacks, colorstr, ops
from ultralytics.utils.checks import check_imgsz, check_imshow
from ultralytics.utils.files import increment_path
from ultralytics.utils.torch_utils import select_device, smart_inference_mode

STREAM_WARNING = """
inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory
errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.

Example:
    results = model(source=..., stream=True)  # generator of Results objects
    for r in results:
        boxes = r.boxes  # Boxes object for bbox outputs
        masks = r.masks  # Masks object for segment masks outputs
        probs = r.probs  # Class probabilities for classification outputs
"""


class BasePredictor:
    """
    A base class for creating predictors.

    This class provides the foundation for prediction functionality, handling model setup, inference,
    and result processing across various input sources.

    Attributes:
        args (SimpleNamespace): Configuration for the predictor.
        save_dir (Path): Directory to save results.
        done_warmup (bool): Whether the predictor has finished setup.
        model (torch.nn.Module): Model used for prediction.
        data (dict): Data configuration.
        device (torch.device): Device used for prediction.
        dataset (Dataset): Dataset used for prediction.
        vid_writer (Dict[str, cv2.VideoWriter]): Dictionary of {save_path: video_writer} for saving video output.
        plotted_img (np.ndarray): Last plotted image.
        source_type (SimpleNamespace): Type of input source.
        seen (int): Number of images processed.
        windows (List[str]): List of window names for visualization.
        batch (tuple): Current batch data.
        results (List[Any]): Current batch results.
        transforms (callable): Image transforms for classification.
        callbacks (Dict[str, List[callable]]): Callback functions for different events.
        txt_path (Path): Path to save text results.
        _lock (threading.Lock): Lock for thread-safe inference.

    Methods:
        preprocess: Prepare input image before inference.
        inference: Run inference on a given image.
        postprocess: Process raw predictions into structured results.
        predict_cli: Run prediction for command line interface.
        setup_source: Set up input source and inference mode.
        stream_inference: Stream inference on input source.
        setup_model: Initialize and configure the model.
        write_results: Write inference results to files.
        save_predicted_images: Save prediction visualizations.
        show: Display results in a window.
        run_callbacks: Execute registered callbacks for an event.
        add_callback: Register a new callback function.
    """

    def __init__(
        self,
        cfg=DEFAULT_CFG,
        overrides: Optional[Dict[str, Any]] = None,
        _callbacks: Optional[Dict[str, List[callable]]] = None,
    ):
        """
        Initialize the BasePredictor class.

        Args:
            cfg (str | dict): Path to a configuration file or a configuration dictionary.
            overrides (dict, optional): Configuration overrides.
            _callbacks (dict, optional): Dictionary of callback functions.
        """
        self.args = get_cfg(cfg, overrides)
        self.save_dir = get_save_dir(self.args)
        if self.args.conf is None:
            self.args.conf = 0.25  # default conf=0.25
        self.done_warmup = False
        if self.args.show:
            self.args.show = check_imshow(warn=True)

        # Usable if setup is done
        self.model = None
        self.data = self.args.data  # data_dict
        self.imgsz = None
        self.device = None
        self.dataset = None
        self.vid_writer = {}  # dict of {save_path: video_writer, ...}
        self.plotted_img = None
        self.source_type = None
        self.seen = 0
        self.windows = []
        self.batch = None
        self.results = None
        self.transforms = None
        self.callbacks = _callbacks or callbacks.get_default_callbacks()
        self.txt_path = None
        self._lock = threading.Lock()  # for automatic thread-safe inference
        callbacks.add_integration_callbacks(self)

    def preprocess(self, im: Union[torch.Tensor, List[np.ndarray]]) -> torch.Tensor:
        """
        Prepare input image before inference.

        Args:
            im (torch.Tensor | List[np.ndarray]): Images of shape (N, 3, H, W) for tensor, [(H, W, 3) x N] for list.

        Returns:
            (torch.Tensor): Preprocessed image tensor of shape (N, 3, H, W).
        """
        not_tensor = not isinstance(im, torch.Tensor)
        if not_tensor:
            im = np.stack(self.pre_transform(im))
            if im.shape[-1] == 3:
                im = im[..., ::-1]  # BGR to RGB
            im = im.transpose((0, 3, 1, 2))  # BHWC to BCHW, (n, 3, h, w)
            im = np.ascontiguousarray(im)  # contiguous
            im = torch.from_numpy(im)

        im = im.to(self.device)
        im = im.half() if self.model.fp16 else im.float()  # uint8 to fp16/32
        if not_tensor:
            im /= 255  # 0 - 255 to 0.0 - 1.0
        return im

    def inference(self, im: torch.Tensor, *args, **kwargs):
        """Run inference on a given image using the specified model and arguments."""
        visualize = (
            increment_path(self.save_dir / Path(self.batch[0][0]).stem, mkdir=True)
            if self.args.visualize and (not self.source_type.tensor)
            else False
        )
        return self.model(im, augment=self.args.augment, visualize=visualize, embed=self.args.embed, *args, **kwargs)

    def pre_transform(self, im: List[np.ndarray]) -> List[np.ndarray]:
        """
        Pre-transform input image before inference.

        Args:
            im (List[np.ndarray]): List of images with shape [(H, W, 3) x N].

        Returns:
            (List[np.ndarray]): List of transformed images.
        """
        same_shapes = len({x.shape for x in im}) == 1
        letterbox = LetterBox(
            self.imgsz,
            auto=same_shapes
            and self.args.rect
            and (self.model.pt or (getattr(self.model, "dynamic", False) and not self.model.imx)),
            stride=self.model.stride,
        )
        return [letterbox(image=x) for x in im]

    def postprocess(self, preds, img, orig_imgs):
        """Post-process predictions for an image and return them."""
        return preds

    def __call__(self, source=None, model=None, stream: bool = False, *args, **kwargs):
        """
        Perform inference on an image or stream.

        Args:
            source (str | Path | List[str] | List[Path] | List[np.ndarray] | np.ndarray | torch.Tensor, optional):
                Source for inference.
            model (str | Path | torch.nn.Module, optional): Model for inference.
            stream (bool): Whether to stream the inference results. If True, returns a generator.
            *args (Any): Additional arguments for the inference method.
            **kwargs (Any): Additional keyword arguments for the inference method.

        Returns:
            (List[ultralytics.engine.results.Results] | generator): Results objects or generator of Results objects.
        """
        self.stream = stream
        if stream:
            return self.stream_inference(source, model, *args, **kwargs)
        else:
            return list(self.stream_inference(source, model, *args, **kwargs))  # merge list of Result into one

    def predict_cli(self, source=None, model=None):
        """
        Method used for Command Line Interface (CLI) prediction.

        This function is designed to run predictions using the CLI. It sets up the source and model, then processes
        the inputs in a streaming manner. This method ensures that no outputs accumulate in memory by consuming the
        generator without storing results.

        Args:
            source (str | Path | List[str] | List[Path] | List[np.ndarray] | np.ndarray | torch.Tensor, optional):
                Source for inference.
            model (str | Path | torch.nn.Module, optional): Model for inference.

        Note:
            Do not modify this function or remove the generator. The generator ensures that no outputs are
            accumulated in memory, which is critical for preventing memory issues during long-running predictions.
        """
        gen = self.stream_inference(source, model)
        for _ in gen:  # sourcery skip: remove-empty-nested-block, noqa
            pass

    def setup_source(self, source):
        """
        Set up source and inference mode.

        Args:
            source (str | Path | List[str] | List[Path] | List[np.ndarray] | np.ndarray | torch.Tensor):
                Source for inference.
        """
        self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2)  # check image size
        self.dataset = load_inference_source(
            source=source,
            batch=self.args.batch,
            vid_stride=self.args.vid_stride,
            buffer=self.args.stream_buffer,
            channels=getattr(self.model, "ch", 3),
        )
        self.source_type = self.dataset.source_type
        if not getattr(self, "stream", True) and (
            self.source_type.stream
            or self.source_type.screenshot
            or len(self.dataset) > 1000  # many images
            or any(getattr(self.dataset, "video_flag", [False]))
        ):  # videos
            LOGGER.warning(STREAM_WARNING)
        self.vid_writer = {}

    @smart_inference_mode()
    def stream_inference(self, source=None, model=None, *args, **kwargs):
        """
        Stream real-time inference on camera feed and save results to file.

        Args:
            source (str | Path | List[str] | List[Path] | List[np.ndarray] | np.ndarray | torch.Tensor, optional):
                Source for inference.
            model (str | Path | torch.nn.Module, optional): Model for inference.
            *args (Any): Additional arguments for the inference method.
            **kwargs (Any): Additional keyword arguments for the inference method.

        Yields:
            (ultralytics.engine.results.Results): Results objects.
        """
        if self.args.verbose:
            LOGGER.info("")

        # Setup model
        if not self.model:
            self.setup_model(model)

        with self._lock:  # for thread-safe inference
            # Setup source every time predict is called
            self.setup_source(source if source is not None else self.args.source)

            # Check if save_dir/ label file exists
            if self.args.save or self.args.save_txt:
                (self.save_dir / "labels" if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)

            # Warmup model
            if not self.done_warmup:
                self.model.warmup(
                    imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, self.model.ch, *self.imgsz)
                )
                self.done_warmup = True

            self.seen, self.windows, self.batch = 0, [], None
            profilers = (
                ops.Profile(device=self.device),
                ops.Profile(device=self.device),
                ops.Profile(device=self.device),
            )
            self.run_callbacks("on_predict_start")
            for self.batch in self.dataset:
                self.run_callbacks("on_predict_batch_start")
                paths, im0s, s = self.batch

                # Preprocess
                with profilers[0]:
                    im = self.preprocess(im0s)

                # Inference
                with profilers[1]:
                    preds = self.inference(im, *args, **kwargs)
                    if self.args.embed:
                        yield from [preds] if isinstance(preds, torch.Tensor) else preds  # yield embedding tensors
                        continue

                # Postprocess
                with profilers[2]:
                    self.results = self.postprocess(preds, im, im0s)
                self.run_callbacks("on_predict_postprocess_end")

                # Visualize, save, write results
                n = len(im0s)
                for i in range(n):
                    self.seen += 1
                    self.results[i].speed = {
                        "preprocess": profilers[0].dt * 1e3 / n,
                        "inference": profilers[1].dt * 1e3 / n,
                        "postprocess": profilers[2].dt * 1e3 / n,
                    }
                    if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:
                        s[i] += self.write_results(i, Path(paths[i]), im, s)

                # Print batch results
                if self.args.verbose:
                    LOGGER.info("\n".join(s))

                self.run_callbacks("on_predict_batch_end")
                yield from self.results

        # Release assets
        for v in self.vid_writer.values():
            if isinstance(v, cv2.VideoWriter):
                v.release()

        # Print final results
        if self.args.verbose and self.seen:
            t = tuple(x.t / self.seen * 1e3 for x in profilers)  # speeds per image
            LOGGER.info(
                f"Speed: %.1fms preprocess, %.1fms inference, %.1fms postprocess per image at shape "
                f"{(min(self.args.batch, self.seen), getattr(self.model, 'ch', 3), *im.shape[2:])}" % t
            )
        if self.args.save or self.args.save_txt or self.args.save_crop:
            nl = len(list(self.save_dir.glob("labels/*.txt")))  # number of labels
            s = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else ""
            LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
        self.run_callbacks("on_predict_end")

    def setup_model(self, model, verbose: bool = True):
        """
        Initialize YOLO model with given parameters and set it to evaluation mode.

        Args:
            model (str | Path | torch.nn.Module, optional): Model to load or use.
            verbose (bool): Whether to print verbose output.
        """
        self.model = AutoBackend(
            weights=model or self.args.model,
            device=select_device(self.args.device, verbose=verbose),
            dnn=self.args.dnn,
            data=self.args.data,
            fp16=self.args.half,
            batch=self.args.batch,
            fuse=True,
            verbose=verbose,
        )

        self.device = self.model.device  # update device
        self.args.half = self.model.fp16  # update half
        if hasattr(self.model, "imgsz"):
            self.args.imgsz = self.model.imgsz  # reuse imgsz from export metadata
        self.model.eval()

    def write_results(self, i: int, p: Path, im: torch.Tensor, s: List[str]) -> str:
        """
        Write inference results to a file or directory.

        Args:
            i (int): Index of the current image in the batch.
            p (Path): Path to the current image.
            im (torch.Tensor): Preprocessed image tensor.
            s (List[str]): List of result strings.

        Returns:
            (str): String with result information.
        """
        string = ""  # print string
        if len(im.shape) == 3:
            im = im[None]  # expand for batch dim
        if self.source_type.stream or self.source_type.from_img or self.source_type.tensor:  # batch_size >= 1
            string += f"{i}: "
            frame = self.dataset.count
        else:
            match = re.search(r"frame (\d+)/", s[i])
            frame = int(match[1]) if match else None  # 0 if frame undetermined

        self.txt_path = self.save_dir / "labels" / (p.stem + ("" if self.dataset.mode == "image" else f"_{frame}"))
        string += "{:g}x{:g} ".format(*im.shape[2:])
        result = self.results[i]
        result.save_dir = self.save_dir.__str__()  # used in other locations
        string += f"{result.verbose()}{result.speed['inference']:.1f}ms"

        # Add predictions to image
        if self.args.save or self.args.show:
            self.plotted_img = result.plot(
                line_width=self.args.line_width,
                boxes=self.args.show_boxes,
                conf=self.args.show_conf,
                labels=self.args.show_labels,
                im_gpu=None if self.args.retina_masks else im[i],
            )

        # Save results
        if self.args.save_txt:
            result.save_txt(f"{self.txt_path}.txt", save_conf=self.args.save_conf)
        if self.args.save_crop:
            result.save_crop(save_dir=self.save_dir / "crops", file_name=self.txt_path.stem)
        if self.args.show:
            self.show(str(p))
        if self.args.save:
            self.save_predicted_images(str(self.save_dir / p.name), frame)

        return string

    def save_predicted_images(self, save_path: str = "", frame: int = 0):
        """
        Save video predictions as mp4 or images as jpg at specified path.

        Args:
            save_path (str): Path to save the results.
            frame (int): Frame number for video mode.
        """
        im = self.plotted_img

        # Save videos and streams
        if self.dataset.mode in {"stream", "video"}:
            fps = self.dataset.fps if self.dataset.mode == "video" else 30
            frames_path = f"{save_path.split('.', 1)[0]}_frames/"
            if save_path not in self.vid_writer:  # new video
                if self.args.save_frames:
                    Path(frames_path).mkdir(parents=True, exist_ok=True)
                suffix, fourcc = (".mp4", "avc1") if MACOS else (".avi", "WMV2") if WINDOWS else (".avi", "MJPG")
                self.vid_writer[save_path] = cv2.VideoWriter(
                    filename=str(Path(save_path).with_suffix(suffix)),
                    fourcc=cv2.VideoWriter_fourcc(*fourcc),
                    fps=fps,  # integer required, floats produce error in MP4 codec
                    frameSize=(im.shape[1], im.shape[0]),  # (width, height)
                )

            # Save video
            self.vid_writer[save_path].write(im)
            if self.args.save_frames:
                cv2.imwrite(f"{frames_path}{frame}.jpg", im)

        # Save images
        else:
            cv2.imwrite(str(Path(save_path).with_suffix(".jpg")), im)  # save to JPG for best support

    def show(self, p: str = ""):
        """Display an image in a window."""
        im = self.plotted_img
        if platform.system() == "Linux" and p not in self.windows:
            self.windows.append(p)
            cv2.namedWindow(p, cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
            cv2.resizeWindow(p, im.shape[1], im.shape[0])  # (width, height)
        cv2.imshow(p, im)
        cv2.waitKey(300 if self.dataset.mode == "image" else 1)  # 1 millisecond

    def run_callbacks(self, event: str):
        """Run all registered callbacks for a specific event."""
        for callback in self.callbacks.get(event, []):
            callback(self)

    def add_callback(self, event: str, func: callable):
        """Add a callback function for a specific event."""
        self.callbacks[event].append(func)
